This study conducts a systematic review to explore the applications of Artificial Intelligence (AI) in mobile learning to support indigenous communities in Malaysia. It also examines the AI techniques used more broadly in education. The main objectives of this research are to investigate the role of Artificial Intelligence (AI) in support the mobile learning and education and provide a taxonomy that shows the stages of process that used in this research and presents the main AI applications that used in mobile learning and education. To identify relevant studies, four reputable databases—ScienceDirect, Web of Science, IEEE Xplore, and Scopus—were systematically searched using predetermined inclusion/exclusion criteria. This screening process resulted in 50 studies which were further classified into groups: AI Technologies (19 studies), Machine Learning (11), Deep Learning (8), Chatbots/ChatGPT/WeChat (4), and Other (8). The results were analyzed taxonomically to provide a structured framework for understanding the diverse applications of AI in mobile learning and education. This review summarizes current research and organizes it into a taxonomy that reveals trends and techniques in using AI to support mobile learning, particularly for indigenous groups in Malaysia.

1. Ahmed, A., Ali, N., Aziz, S., et al. (2021). A review of mobile chatbot apps for anxiety and depression and their self-care features. Computer Methods and Programs in Biomedicine Update, 1, 100012. https://doi.org/10.1016/j.cmpbup.2021.100012
2. Akour, I. A., Al-Maroof, R. S., Alfaisal, R., et al. (2022). A conceptual framework for determining metaverse adoption in higher institutions of gulf area: An empirical study using hybrid SEM-ANN approach. Computers and Education: Artificial Intelligence, 3, 100052. https://doi.org/10.1016/j.caeai.2022.100052
3. Al Ghatrifi, M. O. M., Al Amairi, J. S. S., & Thottoli, M. M. (2023). Surfing the technology wave: An international perspective on enhancing teaching and learning in accounting. Computers and Education: Artificial Intelligence, 4, 100144. https://doi.org/10.1016/j.caeai.2023.100144
4. Alhashmi, S. F., Salloum, S. A., & Abdallah, S. (2019). Critical success factors for implementing artificial intelligence (AI) projects in Dubai government United Arab Emirates (UAE) health sector: Applying the extended technology acceptance model (TAM). In: International Conference on Advanced Intelligent Systems and Informatics. Springer International Publishing. https://doi.org/10.1007/978-3-030-31129-2
5. Alshurafat, H. (2023). The Usefulness and Challenges of Chatbots for Accounting Professionals: Application on ChatGPT. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4345921
6. Alzahrani, A. (2019). Factors that Influence Secondary School Teachers’ Acceptance of E-learning Technologies in Teaching in the Kingdom of Saudi Arabia. Journal of Research in Curriculum Instruction and Educational Technology, 5(2), 175–196. https://doi.org/10.21608/jrciet.2019.33605
7. Ananiadou, S., Thompson, P., & Nawaz, R. (2013). Enhancing search: Events and their discourse context. In: Lecture Notes in Computer Science. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-37256-8
8. Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., et al. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
9. Burleson, W., & Lewis, A. (2016). Optimists’ Creed: Brave New Cyberlearning, Evolving Utopias (Circa 2041). International Journal of Artificial Intelligence in Education, 26(2), 796–808. https://doi.org/10.1007/s40593-016-0096-x
10. Cabrera-Sánchez, J. P., Villarejo-Ramos, Á. F., Liébana-Cabanillas, F., et al. (2020). Identifying relevant segments of AI applications adopters—Expanding the UTAUT2’s variables. Telematics and Informatics, 58, 101529. https://doi.org/10.1016/j.tele.2020.101529
11. Cachero, C., Rico-Juan, J. R., & Macià, H. (2023). Influence of personality and modality on peer assessment evaluation perceptions using Machine Learning techniques. Expert Systems with Applications, 213. https://doi.org/10.1016/j.eswa.2022.119150
12. Chaipidech, P., Srisawasdi, N., Kajornmanee, T., et al. (2022). A personalized learning system-supported professional training model for teachers’ TPACK development. Computers and Education: Artificial Intelligence Journal, 3, 100064.
13. Chen, X., Xie, H., Zou, D., et al. (2020). Application and theory gaps during the rise of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100002. https://doi.org/10.1016/j.caeai.2020.100002
14. Chih-Ming, C., & Ying-You, L. (2020). Developing a computer-mediated communication competence forecasting model based on learning behavior features. Computers and Education: Artificial Intelligence, 1, 100004. https://doi.org/10.1016/j.caeai.2020.100004
15. Ciolacu, M. I., Binder, L., Svasta, P., et al. (2019). Education 4.0—Jump to Innovation with IoT in Higher Education. In: Proceeding of the SIITME 2019—2019 IEEE 25th International Symposium for Design and Technology in Electronic Packaging. https://doi.org/10.1109/SIITME47687.2019.8990825
16. Coelho, O. B., & Silveira, I. (2017). Deep Learning applied to Learning Analytics and Educational Data Mining: A Systematic Literature Review. Anais Do XXVIII Simpósio Brasileiro de Informática Na Educação (SBIE 2017), 1, 143. https://doi.org/10.5753/cbie.sbie.2017.143
17. Cruz-Jesus, F., Castelli, M., Oliveira, T., et al. (2020). Using artificial intelligence methods to assess academic achievement in public high schools of a European Union country. Heliyon, 6(6), e04081. https://doi.org/10.1016/j.heliyon.2020.e04081
18. Donkin, R., Askew, E., & Stevenson, H. (2019). Video feedback and e-Learning enhances laboratory skills and engagement in medical laboratory science students. BMC Medical Education, 19(1), 1–12. https://doi.org/10.1186/s12909-019-1745-1
19. Doornenbal, B. M., Spisak, B. R., & van der Laken, P. A. (2022). Opening the black box: Uncovering the leader trait paradigm through machine learning. Leadership Quarterly, 33(5), 101515. https://doi.org/10.1016/j.leaqua.2021.101515
20. Edwards, B. I., & Cheok, A. D. (2018). Why Not Robot Teachers: Artificial Intelligence for Addressing Teacher Shortage. Applied Artificial Intelligence, 32(4), 345–360. https://doi.org/10.1080/08839514.2018.1464286
21. Engström, E., & Strimling, P. (2020). Deep learning diffusion by infusion into preexisting technologies—Implications for users and society at large. Technology in Society, 63. https://doi.org/10.1016/j.techsoc.2020.101396
22. Fung, P. L., Zaidan, M. A., Timonen, H., et al. (2021). Evaluation of white-box versus black-box machine learning models in estimating ambient black carbon concentration. Journal of Aerosol Science, 152. https://doi.org/10.1016/j.jaerosci.2020.105694
23. Garcia, J. D. R., Leon, J. M., Gonzalez, M. R., et al. (2019). Developing Computational Thinking at School with Machine Learning: An exploration. In: Proceeding of the 2019 International Symposium on Computers in Education. https://doi.org/10.1109/SIIE48397.2019.8970124
24. Gazi, M. A. I., Al Masud, A., Hossain, H., et al. (2024). An investigation on the behavioral intention of existing bank clients in a developing country to use mobile banking services. Journal of Infrastructure, Policy and Development, 8(5), 1–25. https://doi.org/10.24294/jipd.v8i5.3255
25. Golenhofen, N., Heindl, F., Grab-Kroll, C., et al. (2020). The Use of a Mobile Learning Tool by Medical Students in Undergraduate Anatomy and its Effects on Assessment Outcomes. Anatomical Sciences Education, 13(1), 8–18. https://doi.org/10.1002/ase.1878
26. Haderer, B., & Ciolacu, M. (2022). Education 4.0: Artificial Intelligence Assisted Task- and Time Planning System. Procedia Computer Science, 200, 1328–1337. https://doi.org/10.1016/j.procs.2022.01.334
27. Hasanov, A., Laine, T. H., & Chung, T. S. (2019). A survey of adaptive context-aware learning environments. Journal of Ambient Intelligence and Smart Environments, 11(5), 403–428. https://doi.org/10.3233/AIS-190534
28. Hsu, T. C., Abelson, H., Lao, N., et al. (2021). Is it possible for young students to learn the Ai-STEAM application with experiential learning? Sustainability (Switzerland), 13(19). https://doi.org/10.3390/su131911114
29. Hsu, T. C., Chang, C., & Lin, Y. W. (2023). Effects of voice assistant creation using different learning approaches on performance of computational thinking. Computers and Education, 192, 104657. https://doi.org/10.1016/j.compedu.2022.104657
30. Hwang, G. J., Xie, H., Wah, B. W., et al. (2020). Vision, challenges, roles and research issues of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100001.
31. İpek, Z. H., Gözüm, A. İ. C., Papadakis, S., et al. (2023). Educational Applications of the ChatGPT AI System: A Systematic Review Research. Educational Process: International Journal, 12(3), 26–55. https://doi.org/10.22521/edupij.2023.123.2
32. Izkair, A. S., & Lakulu, M. M. (2021). Experience moderator effect on the variables that influence intention to use mobile learning. Bulletin of Electrical Engineering and Informatics, 10(5), 2875–2883. https://doi.org/10.11591/eei.v10i5.3109
33. Izkair, A. S., & Lakulu, M. M. (2023). Model of intention and actual use mobile learning in higher education institutions in Iraq. Indonesian Journal of Electrical Engineering and Computer Science, 30(2), 1250–1258. https://doi.org/10.11591/ijeecs.v30.i2.pp1250-1258
34. Jia, F., Sun, D., Ma, Q., et al. (2022). Developing an AI-Based Learning System for L2 Learners’ Authentic and Ubiquitous Learning in English Language. Sustainability (Switzerland), 14(23). https://doi.org/10.3390/su142315527
35. Kabudi, T., Pappas, I., & Olsen, D. H. (2021). AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2. https://doi.org/10.1016/j.caeai.2021.100017
36. Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004
37. Kneale, P. E. (2018). Where might pedagogic research focus to support students’ education in a REF-TEF world. Journal of Geography in Higher Education, 42(4), 487–497. https://doi.org/10.1080/03098265.2018.1460807
38. Lakshmi, A. J., Kumar, A., Kumar, M. S., et al. (2023). Artificial intelligence in steering the digital transformation of collaborative technical education. Journal of High Technology Management Research, 34(2). https://doi.org/10.1016/j.hitech.2023.100467
39. Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
40. Matzavela, V., & Alepis, E. (2021). Decision tree learning through a Predictive Model for Student Academic Performance in Intelligent M-Learning environments. Computers and Education: Artificial Intelligence, 2. https://doi.org/10.1016/j.caeai.2021.100035
41. McCarthy, J. (2007). From here to human-level AI. Artificial Intelligence, 171(18), 1174–1182. https://doi.org/10.1016/j.artint.2007.10.009
42. Nawaz, R., Thompson, P., & Ananiadou, S. (2012). Identification of Manner in Bio—Events. In Lrec, 3505–3510.
43. Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., et al. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041
44. Okuda, A., & Ofa, S. V. (2018). Artificial intelligence and broadband development through the Asia-Pacific nformation Superhighway. Journal of Infrastructure, Policy and Development, 2(2), 319–342. https://doi.org/10.24294/jipd.v2i2.1047
45. Pedro, F., Subosa, M., Rivas, A., et al. (2019). Artificial Intelligence in Education: Challenges and Opportunities for Sustainable Development Education Sector United Nations Educational, Scientific and Cultural Organization. Ministerio De Educación, 1–46.
46. Poplin, R., Varadarajan, A. V., Blumer, K., et al. (2018). Predicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learning. Nature Biomedical Engineering, 2(3), 158.
47. Qahmash, A. I. M. (2018). The Potentials of Using Mobile Technology in Teaching Individuals with Learning Disabilities: A Review of Special Education Technology Literature. TechTrends, 62(6), 647–653. https://doi.org/10.1007/s11528-018-0298-1
48. Qu, C., & Kim, E. (2022). Dynamic capabilities perspective on innovation ecosystem of China’s universities in the age of artificial intelligence: Policy-based analysis. Journal of Infrastructure, Policy and Development, 6(2). https://doi.org/10.24294/jipd.v6i2.1661
49. Sánchez-Morales, L. N., Alor-Hernández, G., Rosales-Morales, V. Y., et al. (2020). Generating educational mobile applications using UIDPs identified by artificial intelligence techniques. Computer Standards and Interfaces, 70, 103407. https://doi.org/10.1016/j.csi.2019.103407
50. Sanusi, I. T., Olaleye, S. A., Oyelere, S. S., et al. (2022). Investigating learners’ competencies for artificial intelligence education in an African K-12 setting. Computers and Education Open, 3, 100083. https://doi.org/10.1016/j.caeo.2022.100083
51. Sophonhiranrak, S. (2021). Features, barriers, and influencing factors of mobile learning in higher education: A systematic review. Heliyon, 7(4), e06696. https://doi.org/10.1016/j.heliyon.2021.e06696
52. Syed, T. A., Palade, V., Iqbal, R., et al. (2017). A personalized learning recommendation system architecture for learning management system. In: Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. https://doi.org/10.5220/0006513202750282
53. Torres-Diaz, J. C., Duart, J. M., & Hinojosa-Becerra, M. (2018). Plagiarism, internet and academic success at the university. Journal of New Approaches in Educational Research, 7(2), 98–104. https://doi.org/10.7821/naer.2018.7.324
54. Van Brummelen, J. (2019). Conversational Agents to Democratize Artificial Intelligence. In: Proceedings of the 2019 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC. https://doi.org/10.1109/VLHCC.2019.8818805
55. Waheed, H., Hassan, S. U., Aljohani, N. R., et al. (2020). Predicting academic performance of students from VLE big data using deep learning models. Computers in Human Behavior, 104. https://doi.org/10.1016/j.chb.2019.106189
56. Wang, X., Rak, R., Restificar, A., et al. (2011). Detecting experimental techniques and selecting relevant documents for protein-protein interactions from biomedical literature. BMC Bioinformatics, 12. https://doi.org/10.1186/1471-2105-12-S8-S11
57. Wiggberg, M., Gulliksen, J., Cajander, A., et al. (2022). Defining Digital Excellence: Requisite Skills and Policy Implications for Digital Transformation. IEEE Access, 10, 52481–52507. https://doi.org/10.1109/ACCESS.2022.3171924
58. Xin, X., Shu-Jiang, Y., Nan, P., et al. (2022). Review on A big data-based innovative knowledge teaching evaluation system in universities. Journal of Innovation and Knowledge, 7(3), 100197. https://doi.org/10.1016/j.jik.2022.100197
59. Yang, Z., Lv, C., Gan, J., et al. (2019). Application of WeChat Mini Program in Secondary School Students’ Homework Guidance. In: Proceeding of the 2019 IEEE International Conference on Computer Science and Educational Informatization. https://doi.org/10.1109/CSEI47661.2019.8938853
60. Zabasta, A., Zhiravecka, A., Kunicina, N., et al. (2019). Collaborative learning outcomes for creation of industry-oriented curricular: A case study of ERASMUS+ project physics. In: Proceeding of the IEEE Global Engineering Education Conference, EDUCON. https://doi.org/10.1109/EDUCON.2019.8725077
61. Zaky, Y. A. M. (2023). Chatbot Positive Design to Facilitate Referencing Skills and Improve Digital Well-Being. International Journal of Interactive Mobile Technologies, 17(9), 106–126. https://doi.org/10.3991/ijim.v17i09.38395
62. Zawacki-Richter, O., Marín, V. I., Bond, M., et al. (2019). Systematic review of research on artificial intelligence applications in higher education—where are the educators? International Journal of Educational Technology in Higher Education, 6(1). https://doi.org/10.1186/s41239-019-0171-0
63. Zhang, K., & Aslan, A. B. (2021). AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence, 2, 100025. https://doi.org/10.1016/j.caeai.2021.100025